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SUMMARYThis paper studies the impact of a femto-cell underlay deployment that shares radio frequency resources with urban macro-cells. Due to their random and uncoordinated deployment, femto-cells potentially cause destructive interference to macro-cells and vice versa. On the other hand, femto-cells promise to substantially enhance the spectral efficiency due to an increased reuse of radio resources. The performance of networks with indoor Home Evolved NodeB (HeNB) deployment is compared to a system where all users, including indoor users, are served by the outdoor macro Evolved NodeB (eNB). In addition, the impact of closedaccess and open-access femto-cell operation is examined. It is demonstrated that significant capacity gains can be achieved through such HeNB deployment, regardless of whether closed-access or open-access is considered. Results clearly indicate that the capacity gains through femto-cell deployment outweigh the additional interference they introduce.

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The high peaks of an orthogonal frequency division multiplexing (OFDM) signal are distorted nonlinearly by the high power amplifier (HPA). For peak reduction the active constellation extension (ACE) technique [1] can be applied. However, clipping, which is an essential part of ACE, degrades the bit error rate (BER). The degradation is limited by applying the ACE constraints to the clipped signal. In this paper a generalization of the ACE constraints is proposed. In comparison to the conventional constraints a lower out-of-band (OOB) radiation can be achieved. The computational costs for the proposed modification are only very small.

A novel algorithm is proposed to calculate a solution for cross-layer optimization (CLO) between application (APP) layer and medium access control (MAC) layer. The CLO problem is analyzed mathematically and for a related similar optimization problem an explicit solution is found. Based on this solution a deterministic iterative algorithm is deducted, which can approximate the solution of the original problem with little complexity in an upper bounded number of steps. There is an easy way to adapt the algorithm to different objectives, e.g., to maximize the sum of the users' mean opinion scores (MOS) or to limit the outage of users. Multiple examples are given and simulation results are provided.

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